Display of high dynamic range images under varying viewing conditions

Borer, Tim

doi:10.1117/12.2274253

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Borer [36] performed a similar experiment to this work. The effect of ambient lighting and display brightness was investigated, and a model which adapted a gamma function given viewing conditions was presented.…”

Section: B Ambient Lighting On Hdr Displaysmentioning

confidence: 87%

Learning Preferential Perceptual Exposure for HDR Displays

et al. 2019

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High dynamic range (HDR) displays are capable of displaying a wider dynamic range of values than conventional displays. As HDR content becomes more ubiquitous, the use of these displays is likely to accelerate. As HDR displays can present a wider range of values, traditional strategies for mapping HDR content to low dynamic range (LDR) displays can be replaced with either directly displaying values, or using a simple shift mapping (exposure adjustment). The latter approach is especially important when considering ambient lighting, as content viewed in a dark environment may appear substantially different to a bright one. This paper seeks to identify an exposure value which is suitable for displaying specific HDR content on an HDR display under a range of ambient lighting levels. Based on data captured with human participants, this paper establishes user preferred exposure values for a variety of maximum display brightnesses, content and ambient lighting levels. These are then used to develop two models to predict preferred exposure. The first is based on linear regression using straightforward image statistics which require minimal computation and memory to be computed, making this method suitable to be directly used in display hardware. The second is a model based on convolutional neural networks (CNN) to learn image features which best predict exposure values. The CNN model generates better results than the first model at the cost of memory and computation time.INDEX TERMS High dynamic range, machine learning, perception.

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Section: B Ambient Lighting On Hdr Displaysmentioning

confidence: 87%

Learning Preferential Perceptual Exposure for HDR Displays

et al. 2019

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Section: Applications Of the Hdr Spectral Imagementioning

confidence: 99%

“…To alleviate the effects of variable observer viewing conditions and improve the display quality, some studies have attempted to develop IQ enhancement algorithms for HDR images and HDR display devices. 10,11 However, even simple HDR image enhancement algorithms, such as modulation of the EOTF, suffer from quantization imprecision. Specifically, current algorithms require inverse PQ mapping (equivalent to inverse gamma for SDR images) to obtain linear HDR images.…”

Section: Potential Applicationsmentioning

confidence: 99%

“…However, some cutting-edge research in imaging technology still suffers from a permanent lack of reliable images. For instance, converting a single standard dynamic range (SDR) image to a high dynamic range (HDR) image 7 – 9 and the electro-optical transfer function (EOTF) modulation for HDR display enhancement 10 , 11 require massive HDR images as well as their SDR versions for model training and quality evaluation. The HDR image-based IQ evaluation also demands HDR images of which the EOTF and color appearance can be adjusted for further research 12 .…”

Section: Introductionmentioning

confidence: 99%

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Constructing high-dynamic-range spectral images for natural scenes

Ye,

Xu,

Huang

et al. 2023

Opt. Eng.

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“…In addition to studies about detection thresholds, such as contrast sensitivity and TVI, there are some works that study the effect of background or surround luminance on various factors, such as the effect on perceived brightness [BB67, YLT*19], perceived dynamic range [HTM*19] and chromatic adaptation [WZIC19]. Background or surround luminance may also influence several aspects when perceiving natural images, such as perceived contrast of images measured by adjusting gamma [Bor17], subjective quality of tone mapping operators [MBDC15] and user preferences for HDR displays [RHLM09].…”

Section: Related Workmentioning

confidence: 99%

Modelling Surround‐aware Contrast Sensitivity for HDR Displays

Jeon

Serrano

et al. 2022

Computer Graphics Forum

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Despite advances in display technology, many existing applications rely on psychophysical datasets of human perception gathered using older, sometimes outdated displays. As a result, there exists the underlying assumption that such measurements can be carried over to the new viewing conditions of more modern technology. We have conducted a series of psychophysical experiments to explore contrast sensitivity using a state‐of‐the‐art HDR display, taking into account not only the spatial frequency and luminance of the stimuli but also their surrounding luminance levels. From our data, we have derived a novel surround‐aware contrast sensitivity function (CSF), which predicts human contrast sensitivity more accurately. We additionally provide a practical version that retains the benefits of our full model, while enabling easy backward compatibility and consistently producing good results across many existing applications that make use of CSF models. We show examples of effective HDR video compression using a transfer function derived from our CSF, tone‐mapping and improved accuracy in visual difference prediction.

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Display of high dynamic range images under varying viewing conditions

Cited by 5 publications

References 4 publications

Learning Preferential Perceptual Exposure for HDR Displays

Learning Preferential Perceptual Exposure for HDR Displays

Constructing high-dynamic-range spectral images for natural scenes

Modelling Surround‐aware Contrast Sensitivity for HDR Displays

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